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Comment by trustno2 | Hacker News Reader

I'm not a great programmer, but I think I'm quite good, I have contributions in quite a lot of foss projects, I have CS masters, yet I have no idea how would I even start these FAANG problems. In 30 minutes? Who is this optimizing for?

I worked in one "unicorn" once and all I did was getting protobufs from one API, putting it to database and taking it back from database and putting it to different API; it was so boring. I solved my boredom by contributing to some in-house framework, after which I was told I am out of my line and should go back to copying data between APIs.

Are people at Facebook actually solving these hard CS problems in daily life? Why are these interviews even a thing?

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Many people are not. These trivia-style questions _used to_ be given to both assess problem-solving skills and identify people who were capable of doing the actual CS needed to solve the core problems these companies were dealing with. These days, these problems are mostly gate keeping IMO.

To be fair, gate keeping is the literal point of the interview process but I think I understand what you mean.

Is it fair to set the bar higher than what it was set for you to be hired into a company? Yeah, I think so.

The problem though is that leetcode challenges are shallow, and don't measure the applicant's ability to understand complex issues or algorithms. Most code is not solved once in 45 minutes, but iterated on multiple times.

"I solved a leetcode issue, so you have to solve one too if you want to be hired on" I think is the kind of gatekeeping you're talking about.

It’s filtering on dedication and desperation.

It’s why so many in the tech industry are H1B. There’s no lack of domestic talent but domestic talent doesn’t need a visa to stay in the country. So, they’ll not go through the insane process and just take a normal job that doesn’t have as insane of a hiring bar.

To be fair the ones who do succeed in those FAANG interviews are probably pretty smart, good under pressure and know algorithms / data structures very well. I'd have a hard time passing these interviews even with a lot of preparation - I don't excel under pressure nor do I have the will to prepare for those interviews for months (which is what it would take for me to reach 'FAANG' level interview taking and I'm being generous here, it could take me more than months). The vast majority of candidates are like me - some combination of time constraints and lack of superior intelligence will prevent them for excelling in those interviews or even trying. I'm just not in the top 2% of devs in terms of intelligence and am not particularly great in charming my way into an offer, nor am I a member of a protected group / DEI. So knowing my chances are low to begin with I'm pretty much fine with deciding FAANG is not very realistic for me.

> The vast majority of candidates are like me - some combination of time constraints and lack of superior intelligence will prevent them for excelling in those interviews or even trying.The vast majority of candidates are like me - some combination of time constraints and lack of superior intelligence will prevent them for excelling in those interviews or even trying.

Surely that's an attitude thing more than a true limitation? Just because the hill is steeper for you doesn't mean you can't still get to the top.

Could be. Or it could be I'm just being realistic about my abilities - I mean statistically the vast majority of candidates don't make it to FAANG, you have less than a 1% chance of getting an offer. Even if you try all of them that's not great odds - 5%. Even doing this rotation twice, still not great - 10%. Now sure, you can make the odds work better for you by preparing more than the others, but there will be at least a few dozen other candidates that know this just as well as you do and will cram for these interviews just as hard. What are the chances you're smarter / better prepared / better interviewee than all of them? I'm sure there are many candidates who studies hard, are reasonably intelligent and just can't make the cut. That's what the statistics say at least. Now for me the worst part in interviewing is the nervousness, in a 5 round interview I'll have to be in amazing shape to not blow any of them up. It has never happened to me before that I could stay focused and nerves free for 5+ hours straight during a series of interviews and I'm talking about much easier interviews than what Google/Meta throws at you. And I've gone through at least 50 interviews with all kinds of companies over the years.

It's just very difficult. I'm not saying people shouldn't try difficult things - I did, and I do and I will. But also being realistic about your abilities and chances is important.

Now not all FAANG is the same, for example Microsoft (who isn't part of FAANG apparently?) where I live isn't that hard to get into. Hard yes but is less notorious than the others. But I'm talking in general here.

At this point I wonder if I should tell you explaining your statistics are incorrect. It's actually worse than you point out.

Let's say you have a 1% of getting hired. And getting 2 offers is still getting hired, right? So the odds of getting hired after N interviews is:

1 - (1 - P)^N.

(because you have to be in the "not hired" 99% bucket every time)

For 5 interviews with P=1% => 4.9%

For 10 interviews with P=1% => 9.6%

For 20 interviews => 18.2%

50 interviews => 39.5%

It takes more than 68 interviews to get to a 50% chance of getting hired. And of course, the number of interviews can be infinite and you still won't have 100% odds.

Lol 4.9% I stand corrected then! My 1% chance figure is also pulled out of thin air, I have no idea how many candidates are being interviewed for a position. I'm sure there are thousands who are applying and much less than that get to the interview stage. Do Google/Meta/etc actually make the investment of interviewing hundreds of candidates for each SWE position? I have no idea.

Exactly.

The typical day-day is always something like 'munge the data from this input stream so it can be processed by this analysis service and then uploaded to snowflake' or whatever.

Very rare you need any DSA (other than being aware of memory/performance implications of certain datastructures, etc) let alone DP.

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